Although the scientific literature consists of over 10,000 papers on eHealth, remarkably few applications are consistently being used in the healthcare domain. Numerous reasons for this lack of progression have been noted, one of these being the objection of medical professionals to the introduction of interventions that are supposedly lacking evidence of their effectiveness. A study of existing literature and, especially, literature reviews confirms that there does not yet exist scientific evidence of the effectiveness of eHealth. But, this study also comes across insights in the reasons why scientific evidence is hard to come by and possible future directions for healthcare organisations how to take advantage of eHealth despite the current lack of interventions that are truly evidence-based and for eHealth researchers to build collectively a stronger evidence-based case for eHealth interventions.
BACKGROUND: Early accurate assessment of burn depth is important to determine the optimal treatment of burns. The method most used to determine burn depth is clinical assessment, which is the least expensive, but not the most accurate.Laser Doppler imaging (LDI) is a technique with which a more accurate (>95%) estimate of burn depth can be made by measuring the dermal perfusion. The actual effect on therapeutic decisions, clinical outcomes and the costs of the introduction of this device, however, are unknown. Before we decide to implement LDI in Dutch burn care, a study on the effectiveness and cost-effectiveness of LDI is necessary.METHODS/DESIGN: A multicenter randomised controlled trial will be conducted in the Dutch burn centres: Beverwijk, Groningen and Rotterdam. All patients treated as outpatient or admitted to a burn centre within 5 days post burn, with burns of indeterminate depth (burns not obviously superficial or full thickness) and a total body surface area burned of ≤ 20% are eligible. A total of 200 patients will be included. Burn depth will be diagnosed by both clinical assessment and laser Doppler imaging between 2-5 days post burn in all patients. Subsequently, patients are randomly divided in two groups: 'new diagnostic strategy' versus 'current diagnostic strategy'. The results of the LDI-scan will only be provided to the treating clinician in the 'new diagnostic strategy' group. The main endpoint is the effect of LDI on wound healing time.In addition we measure: a) the effect of LDI on other patient outcomes (quality of life, scar quality), b) the effect of LDI on diagnostic and therapeutic decisions, and c) the effect of LDI on total (medical and non-medical) costs and cost-effectiveness.DISCUSSION: This trial will contribute to our current knowledge on the use of LDI in burn care and will provide evidence on its cost-effectiveness.TRIAL REGISTRATION: NCT01489540.
Background: Patient education, advice on returning to normal activities and (home-based) exercise therapy are established treatment options for patients with non-specific low back pain (LBP). However, the effectiveness of physiotherapy interventions on physical functioning and prevention of recurrent events largely depends on patient self-management, adherence to prescribed (home-based) exercises and recommended physical activity behaviour. Therefore we have developed e-Exercise LBP, a blended intervention in which a smartphone application is integrated within face-to-face care. E-Exercise LBP aims to improve patient self-management skills and adherence to exercise and physical activity recommendations and consequently improve the effectiveness of physiotherapy on patients’ physical functioning. The aim of this study is to investigate the short- (3 months) and long-term (12 and 24 months) effectiveness on physical functioning and cost-effectiveness of e-Exercise LBP in comparison to usual primary care physiotherapy in patients with LBP. Methods: This paper presents the protocol of a prospective, multicentre cluster randomized controlled trial. In total 208 patients with LBP pain were treated with either e-Exercise LBP or usual care physiotherapy. E-Exercise LBP is stratified based on the risk for developing persistent LBP. Physiotherapists are able to monitor and evaluate treatment progress between face-to-face sessions using patient input from the smartphone application in order to optimize physiotherapy care. The smartphone application contains video-supported self-management information, video-supported exercises and a goal-oriented physical activity module. The primary outcome is physical functioning at 12-months follow-up. Secondary outcomes include pain intensity, physical activity, adherence to prescribed (home-based) exercises and recommended physical activity behaviour, self-efficacy, patient activation and health-related quality of life. All measurements will be performed at baseline, 3, 12 and 24months after inclusion. An economic evaluation will be performed from the societal and the healthcare perspective and will assess cost-effectiveness of e-Exercise LBP compared to usual physiotherapy at 12 and 24months. Discussion: A multi-phase development and implementation process using the Center for eHealth Research Roadmap for the participatory development of eHealth was used for development and evaluation. The findings will provide evidence on the effectiveness of blended care for patients with LBP and help to enhance future implementation of blended physiotherapy.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
Nature areas in North-West Europe (NWE) face an increasing number of visitors (intensified by COVID-19) resulting in an increased pressure on nature, negative environmental impacts, higher management costs, and nuisance for local residents and visitors. The high share of car use exaggerates these impacts, including peak pressures. Furthermore, the almost exclusive access by car excludes disadvantaged people, specifically those without access to a car. At the same time, the urbanised character of NWE, its dense public transport network, well-developed tourism & recreation sector, and presence of shared mobility providers offers ample opportunities for more sustainable tourism. Thus, MONA will stimulate sustainable tourism in and around nature areas in NWE which benefits nature, the environment, visitors, and the local economy. MONA will do so by encouraging a modal shift through facilitating sustainableThe pan-European Innovation Action, funded under the Horizon Europe Framework Programme, aims to promote innovative governance processes ,and help public authorities in shaping their climate mitigation and adaptation policies. To achieve this aim, the GREENGAGE project will leverage citizens’ participation and equip them with innovative digital solutions that will transform citizen’s engagement and cities’ effectiveness in delivering the European Green Deal objectives for carbon neutral cities.Focusing on mobility, air quality and healthy living, citizens will be inspired to observe and co-create their cities by sensing their urban environments. The aim to complement, validate, and enrich information in authoritative data held by the public administrations and public agencies. This will be facilitated by engaging with citizens to co-create green initiatives and to develop Citizen Observatories. In GREENGAGE, Citizen Observatories will be a place where pilot cities will co-examine environmental issues integrating novel bottom-up process with top-down perspectives. This will provide the basis to co-create and co-design innovative solutions to monitor environmental problems at ground level with the help of citizens.With two interrelated project dimensions, the project aims to enhance intelligence applied to city decision-making processes and governance by engaging with citizen observations integrated with Copernicus, GEOSS, in-situ, and socio-economic intelligence, and by delivering innovative governance models based on novel toolboxes of decision-making methodologies and technologies. The envisioned citizens observatory campaigns will be deployed and fully demonstrated in 5 pilot engagements in selected European cities and regions including: Bristol (the United Kingdom), Copenhagen (Denmark), Turano / Gerace (Italy) and the region of Noord Brabant (the Netherlands). These innovation pilots aim to highlight the need for smart city governance by promoting citizen engagement, co-creation, gathering new data which will complement existing datasets and evidence-based decision and policymaking.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
